A primary aim of microbial ecology is to determine patterns and drivers of community distribution, interaction, and assembly amidst complexity and uncertainty. Microbial community composition has been shown to change across gradients of environment, geographic distance, salinity, temperature, oxygen, nutrients, pH, day length, and biotic factors 1-6 . These patterns have been identified mostly by focusing on one sample type and region at a time, with insights extra polated across environments and geography to produce generalized principles. To assess how microbes are distributed across environments globally-or whether microbial community dynamics follow funda mental ecological 'laws' at a planetary scale-requires either a massive monolithic cross environment survey or a practical methodology for coordinating many independent surveys. New studies of microbial environments are rapidly accumulating; however, our ability to extract meaningful information from across datasets is outstripped by the rate of data generation. Previous meta analyses have suggested robust gen eral trends in community composition, including the importance of salinity 1 and animal association 2 . These findings, although derived from relatively small and uncontrolled sample sets, support the util ity of meta analysis to reveal basic patterns of microbial diversity and suggest that a scalable and accessible analytical framework is needed.The Earth Microbiome Project (EMP, http://www.earthmicrobiome. org) was founded in 2010 to sample the Earth's microbial communities at an unprecedented scale in order to advance our understanding of the organizing biogeographic principles that govern microbial commu nity structure 7,8 . We recognized that open and collaborative science, including scientific crowdsourcing and standardized methods 8 , would help to reduce technical variation among individual studies, which can overwhelm biological variation and make general trends difficult to detect 9 . Comprising around 100 studies, over half of which have yielded peer reviewed publications (Supplementary Table 1), the EMP has now dwarfed by 100 fold the sampling and sequencing depth of earlier meta analysis efforts 1,2 ; concurrently, powerful analysis tools have been developed, opening a new and larger window into the distri bution of microbial diversity on Earth. In establishing a scalable frame work to catalogue microbiota globally, we provide both a resource for the exploration of myriad questions and a starting point for the guided acquisition of new data to answer them. As an example of using this Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of r...

Scaling laws underpin unifying theories of biodiversity and are among the most predictively powerful relationships in biology. However, scaling laws developed for plants and animals often go untested or fail to hold for microorganisms. As a result, it is unclear whether scaling laws of biodiversity will span evolutionarily distant domains of life that encompass all modes of metabolism and scales of abundance. Using a global-scale compilation of ∼35,000 sites and ∼5.6·10 6 species, including the largest ever inventory of high-throughput molecular data and one of the largest compilations of plant and animal community data, we show similar rates of scaling in commonness and rarity across microorganisms and macroscopic plants and animals. We document a universal dominance scaling law that holds across 30 orders of magnitude, an unprecedented expanse that predicts the abundance of dominant ocean bacteria. In combining this scaling law with the lognormal model of biodiversity, we predict that Earth is home to upward of 1 trillion (10 12 ) microbial species. Microbial biodiversity seems greater than ever anticipated yet predictable from the smallest to the largest microbiome.biodiversity | microbiology | macroecology | microbiome | rare biosphere T he understanding of microbial biodiversity has rapidly transformed over the past decade. High-throughput sequencing and bioinformatics have expanded the catalog of microbial taxa by orders of magnitude, whereas the unearthing of new phyla is reshaping the tree of life (1-3). At the same time, discoveries of novel forms of metabolism have provided insight into how microbes persist in virtually all aquatic, terrestrial, engineered, and host-associated ecosystems (4, 5). However, this period of discovery has uncovered few, if any, general rules for predicting microbial biodiversity at scales of abundance that characterize, for example, the ∼10 14 cells of bacteria that inhabit a single human or the ∼10 30 cells of bacteria and archaea estimated to inhabit Earth (6, 7). Such findings would aid the estimation of global species richness and reveal whether theories of biodiversity hold across all scales of abundance and whether socalled law-like patterns of biodiversity span the tree of life.A primary goal of ecology and biodiversity theory is to predict diversity, commonness, and rarity across evolutionarily distant taxa and scales of space, time, and abundance (8-10). This goal can hardly be achieved without accounting for the most abundant, widespread, and metabolically, taxonomically, and functionally diverse organisms on Earth (i.e., microorganisms). However, tests of biodiversity theory rarely include both microbial and macrobial datasets. At the same time, the study of microbial ecology has yet to uncover quantitative relationships that predict diversity, commonness, and rarity at the scale of host microbiomes and beyond. These unexplored opportunities leave the understanding of biodiversity limited to the most conspicuous species of plants and animals. This lack of synthesi...

The species abundance distribution (SAD) is one of the most intensively studied distributions in ecology and its hollow-curve shape is one of ecology's most general patterns. We examine the SAD in the context of all possible forms having the same richness (S) and total abundance (N), i.e. the feasible set. We find that feasible sets are dominated by similarly shaped hollow curves, most of which are highly correlated with empirical SADs (most R(2) values > 75%), revealing a strong influence of N and S on the form of the SAD and an a priori explanation for the ubiquitous hollow curve. Empirical SADs are often more hollow and less variable than the majority of the feasible set, revealing exceptional unevenness and relatively low natural variability among ecological communities. We discuss the importance of the feasible set in understanding how general constraints determine observable variation and influence the forms of predicted and empirical patterns.

Translating the ever-increasing wealth of information on microbiomes (environment, host or built environment) to advance our understanding of system-level processes is proving to be an exceptional research challenge. One reason for this challenge is that relationships between characteristics of microbiomes and the system-level processes that they influence are often evaluated in the absence of a robust conceptual framework and reported without elucidating the underlying causal mechanisms. The reliance on correlative approaches limits the potential to expand the inference of a single relationship to additional systems and advance the field. We propose that research focused on how microbiomes influence the systems they inhabit should work within a common framework and target known microbial processes that contribute to the system-level processes of interest. Here, we identify three distinct categories of microbiome characteristics (microbial processes, microbial community properties and microbial membership) and propose a framework to empirically link each of these categories to each other and the broader system-level processes that they affect. We posit that it is particularly important to distinguish microbial community properties that can be predicted using constituent taxa (community-aggregated traits) from those properties that cannot currently be predicted using constituent taxa (emergent properties). Existing methods in microbial ecology can be applied to more explicitly elucidate properties within each of these three categories of microbial characteristics and connect them with each other. We view this proposed framework, gleaned from a breadth of research on environmental microbiomes and ecosystem processes, as a promising pathway with the potential to advance discovery and understanding across a broad range of microbiome science.

Scaling laws underpin unifying theories of biodiversity and are among the most predictively powerful relationships in biology. However, scaling laws developed for plants and animals often go untested or fail to hold for microorganisms. As a result, it is unclear whether scaling laws of biodiversity will span evolutionarily distant domains of life that encompass all modes of metabolism and scales of abundance. Using a global-scale compilation of ~35,000 sites and ~5.6·106 species, including the largest ever inventory of high-throughput molecular data and one of the largest compilations of plant and animal community data, we demonstrate similar rates of scaling in commonness and rarity across microorganisms and macroscopic plants and animals. We document a universal dominance scaling law that holds across 30 orders of magnitude, an unprecedented expanse that predicts the abundance of dominant ocean bacteria. In combining this scaling law with the lognormal model of biodiversity, we predict that Earth is home to upwards one trillion (1012) microbial species. Microbial biodiversity seems greater than ever anticipated yet predictable from the smallest to the largest microbiome.

Scaling laws underpin unifying theories of biodiversity and are among the most predictively powerful relationships in biology. However, scaling laws developed for plants and animals often go untested or fail to hold for microorganisms. As a result, it is unclear whether scaling laws of biodiversity span evolutionarily distant domains of life that encompass all modes of metabolism and scales of abundance. Using a global-scale compilation of ~35,000 sites and ~5.6·106 species, we demonstrate similar rates of scaling in commonness and rarity across microorganisms and macroscopic plants and animals. We document a universal dominance scaling law that holds across 30 orders of magnitude, an unprecedented expanse that predicts the abundance of dominant ocean bacteria. In combining this scaling law with the lognormal model of species abundance, we predict that Earth is home to ~1012 microbial species. This estimate is also supported by the microbial richness scaling relationship we derive from the largest ever inventory of high-throughput molecular data. Microbial biodiversity seems greater than ever anticipated yet predictable from the smallest to the largest microbiome.

Microorganisms are the most abundant, diverse and functionally important organisms on Earth. Over the past decade, microbial ecologists have produced the largest ever community datasets. However, these data are rarely used to uncover law-like patterns of commonness and rarity, test theories of biodiversity, or explore unifying explanations for the structure of microbial communities. Using a global scale compilation of >20,000 samples from environmental, engineered and host-related ecosystems, we test the power of competing theories to predict distributions of microbial abundance and diversity-abundance scaling laws. We show that these patterns are best explained by the synergistic interaction of stochastic processes that are captured by lognormal dynamics. We demonstrate that lognormal dynamics have predictive power across scales of abundance, a criterion that is essential to biodiversity theory. By understanding the multiplicative and stochastic nature of ecological processes, scientists can better understand the structure and dynamics of Earth's largest and most diverse ecological systems.

Scaling laws underpin unifying theories of biodiversity and are among the most predictively powerful relationships in biology. However, scaling laws developed for plants and animals often go untested or fail to hold for microorganisms. As a result, it is unclear whether scaling laws of biodiversity will span evolutionarily distant domains of life that encompass all modes of metabolism and scales of abundance. Using a global-scale compilation of ~35,000 sites and ~5.6·106 species, including the largest ever inventory of high-throughput molecular data and one of the largest compilations of plant and animal community data, we demonstrate similar rates of scaling in commonness and rarity across microorganisms and macroscopic plants and animals. We document a universal dominance scaling law that holds across 30 orders of magnitude, an unprecedented expanse that predicts the abundance of dominant ocean bacteria. In combining this scaling law with the lognormal model of biodiversity, we predict that Earth is home to upwards one trillion (1012) microbial species. Microbial biodiversity seems greater than ever anticipated yet predictable from the smallest to the largest microbiome.